
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "143"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 25 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 25 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2460008	RF_maintenance	100.00%	92.11%	100.00%	0.00%	-	-	15.345234	18.492904	14.051937	15.282599	6.156066	7.724189	4.206563	5.638860	0.1261	0.0330	0.0722	nan	nan
2460007	RF_maintenance	100.00%	98.97%	100.00%	0.00%	-	-	11.328995	13.862043	11.001428	11.951281	5.500872	7.200084	0.987078	2.406151	0.1107	0.0308	0.0611	nan	nan
2459999	RF_maintenance	0.00%	98.91%	99.08%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.3866	0.3616	0.2837	nan	nan
2459998	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	9.724569	11.736201	9.395949	10.105374	7.382223	10.238835	0.244660	1.620620	0.0940	0.0293	0.0500	nan	nan
2459997	RF_maintenance	100.00%	99.95%	100.00%	0.00%	-	-	10.681797	12.780584	9.955796	10.866631	7.180197	9.583013	1.000721	2.662670	0.1013	0.0306	0.0540	nan	nan
2459996	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	11.917890	13.735999	12.496926	13.312289	6.749671	9.215735	0.193702	1.251763	0.0978	0.0297	0.0514	nan	nan
2459995	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	12.040499	13.921354	11.618993	12.515307	7.404236	9.493984	0.094594	1.023508	0.0981	0.0326	0.0480	nan	nan
2459994	RF_maintenance	100.00%	99.95%	100.00%	0.00%	-	-	11.490903	13.527284	10.043758	10.955631	7.330095	9.559735	0.283020	1.143262	0.0903	0.0300	0.0443	nan	nan
2459993	RF_maintenance	100.00%	98.02%	100.00%	0.00%	-	-	12.745323	12.598455	9.347395	10.160915	9.743567	10.933683	0.674128	2.482466	0.0620	0.0269	0.0247	nan	nan
2459991	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	13.564125	15.768363	9.902520	10.753184	8.662094	10.764823	-0.130430	0.718235	0.0900	0.0298	0.0469	nan	nan
2459990	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.542706	-0.922843	-1.291194	-0.990313	-0.087026	2.229106	0.953966	0.508207	0.6436	0.6515	0.3719	nan	nan
2459989	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.595839	-0.928895	-0.971959	-0.645991	-0.270344	1.663620	-0.264073	0.472368	0.6400	0.6492	0.3737	nan	nan
2459988	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.613557	-1.444982	-1.375626	-0.999803	0.067056	1.679321	-0.466096	-0.482206	0.6422	0.6536	0.3696	nan	nan
2459987	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.274535	-0.580630	-1.345202	-0.409856	-0.431798	0.698996	-0.386303	-0.429979	0.6493	0.6592	0.3683	nan	nan
2459986	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.485042	-1.081231	-1.491356	-0.721897	1.021201	0.653070	-0.160608	-0.840982	0.6701	0.6813	0.3220	nan	nan
2459985	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.351680	-0.674629	-1.047241	-0.248806	0.206079	2.910099	1.279445	0.904266	0.6502	0.6584	0.3748	nan	nan
2459984	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	0.048267	-0.505061	-1.014882	1.060399	-0.016512	0.730838	0.421292	1.217806	0.6617	0.6648	0.3450	nan	nan
2459983	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.680912	-0.798807	-1.396845	-0.808838	-0.087483	0.169959	-0.744559	-1.170921	0.6756	0.6935	0.3120	nan	nan
2459982	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-0.759973	-0.956911	-1.098747	-0.621492	0.168921	1.552350	-0.425936	-0.709725	0.7306	0.7288	0.2686	nan	nan
2459981	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	-0.672379	-0.817905	-1.420221	-1.444661	0.822841	20.987213	-0.424065	-0.296479	0.6525	0.6602	0.3688	nan	nan
2459980	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-1.064668	-0.437236	-0.873415	0.891642	-0.700396	2.742000	-1.120418	0.338259	0.6926	0.6937	0.2997	nan	nan
2459979	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	-1.349526	-0.238584	-0.710680	1.091391	-0.452390	4.930280	-0.862361	-1.523604	0.6469	0.6592	0.3724	nan	nan
2459978	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	-1.267606	-0.173129	-0.668600	1.261583	-0.297056	5.074263	-0.678148	-1.719754	0.6480	0.6580	0.3783	nan	nan
2459977	digital_ok	0.00%	0.00%	0.00%	0.00%	-	-	-1.271027	-0.081811	-0.755024	0.834501	0.096400	2.208843	-0.587333	-1.505360	0.6142	0.6226	0.3427	nan	nan
2459976	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	-1.281002	-0.108416	-0.819113	1.238485	-0.232641	7.365041	-0.772176	-1.297722	0.6566	0.6643	0.3666	nan	nan

Antenna Season Report Notebook¶

Antenna 143 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 143, Node N14:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 143: 2460008

Antenna 143: 2460007

Antenna 143: 2459999

Antenna 143: 2459998

Antenna 143: 2459997

Antenna 143: 2459996

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Antenna 143: 2459979

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Antenna 143: 2459977

Antenna 143: 2459976

Antenna Season Report Notebook¶

Antenna 143 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 143, Node N14:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 143: 2460008

Antenna 143: 2460007

Antenna 143: 2459999

Antenna 143: 2459998

Antenna 143: 2459997

Antenna 143: 2459996

Antenna 143: 2459995

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Antenna 143: 2459980

Antenna 143: 2459979

Antenna 143: 2459978

Antenna 143: 2459977

Antenna 143: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶